title: Fixed- and random-effects meta-analytic structural equation modeling: examples and analyses in R.
abstract: Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation Modeling 16:28-53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed.
authors.affiliation: Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Block AS4, Level 2, 9 Arts Link, Singapore, 117570, Singapore, mikewlcheung@nus.edu.sg.
authors.forename: Mike W-L
authors.initials: MW
authors.lastname: Cheung
mesh_terms: D001499:Bayes Theorem; D019985:Benchmarking / Q000379:methods; D016010:Binomial Distribution; D003196:Computer Graphics; D006801:Humans; D015201:Meta-Analysis as Topic; D008960:Models, Psychological; D015233:Models, Statistical; D011381:Programming Languages; D011930:Reaction Time / Q000502:physiology; D012984:Software